Recognizing Semantic Locations from Smartphone Log with Combined Machine Learning Techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Smartphones, equipped with powerful processors, accelerometers, compasses and Global Positioning Systems (GPS) receivers, have favored the increase of location and context-based services over the last years. Many researchers have attempted to recognize user's semantic location by various methods. The traditional semantic location recognition methods require partitioning the location and registering all the information to construct Wi-Fi map for fine localization, which is not practical in daily life and continuous attempt for recognizing semantic location makes smartphone battery last shorter time. Even worse, they have low accuracy when recognizing the locations located near to each other. In this paper, we propose a hybrid location recognition approach. The proposed method combines k-nearest neighbor with decision tree to recognize semantic locations. It consists of moving status detection, indoor/outdoor environment check and location recognition which consists of k-nearest neighbor (kNN) and decision tree. The proposed method can be used in indoor or dense urban environments where traditional approaches fail. Finally, the proposed method is practically developed over Android smartphones and tested in terms of performance. The experiments show the usefulness of the proposed semantic location recognition method.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014
EditorsYu Zheng, Parimala Thulasiraman, Bernady O. Apduhan, Yukikazu Nakamoto, Huansheng Ning, Yuqing Sun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-71
Number of pages6
ISBN (Electronic)9781479976461
DOIs
Publication statusPublished - 2014
Event11th IEEE International Conference on Ubiquitous Intelligence and Computing and 11th IEEE International Conference on Autonomic and Trusted Computing and 14th IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014 - Denpasar, Bali, Indonesia
Duration: 2014 Dec 92014 Dec 12

Publication series

NameProceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014

Other

Other11th IEEE International Conference on Ubiquitous Intelligence and Computing and 11th IEEE International Conference on Autonomic and Trusted Computing and 14th IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014
CountryIndonesia
CityDenpasar, Bali
Period14/12/914/12/12

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Xu, H., & Cho, S. B. (2014). Recognizing Semantic Locations from Smartphone Log with Combined Machine Learning Techniques. In Y. Zheng, P. Thulasiraman, B. O. Apduhan, Y. Nakamoto, H. Ning, & Y. Sun (Eds.), Proceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014 (pp. 66-71). [7306935] (Proceedings - 2014 IEEE International Conference on Ubiquitous Intelligence and Computing, 2014 IEEE International Conference on Autonomic and Trusted Computing, 2014 IEEE International Conference on Scalable Computing and Communications and Associated Symposia/Workshops, UIC-ATC-ScalCom 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UIC-ATC-ScalCom.2014.128